In the post-Moore's law era, the progress of electronics relies on discovering superior
semiconductor materials and optimizing device fabrication. Computational methods …

Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …

The combination of deep learning and ab initio materials calculations is emerging as a
trending frontier of materials science research, with deep-learning density functional theory …

Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research, which …

Realizing large materials models has emerged as a critical endeavor for materials research
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …

Squaric-acid-linked zwitterionic covalent organic frameworks (Z-COFs), assembled through
interlayer interactions, are emerging as potential materials in the field of photocatalysis …

Deep learning electronic structures from ab initio calculations holds great potential to
revolutionize computational materials studies. While existing methods proved success in …

The use of machine learning methods for predicting the properties of crystalline materials
encounters significant challenges, primarily related to input encoding, output versatility, and …

A new material descriptor to predict the work function of the high-k gate material of a
junctionless transistor. This descriptor model is focused on vectorizing property metrics and …